The world of healthcare can be chaotic, with all the prescriptions, treatments, and just about everything in between. Step forward artificial intelligence (AI), which many have predicted will help us through the complicated world of healthcare. So, is this the case and are there any drawbacks to using AI in the medical field?
AI enhances nearly every field that it touches, with the world of healthcare being no exception. It is basically the ability of computers and machines to use features generally associated with intelligence and humans, such as learning problem-solving and reasoning to process data. In the context of healthcare this means AI can be used to help doctors recognize and diagnose diseases much faster and provide much more effective treatments for such medical conditions. For example, a project at University College London used an algorithm, which can go through large volumes of medical data and predict which patients are most likely to suffer from a fatal premature heart attack. This allows doctors to detect problems earlier and increase the overall effectiveness of treatments.
However, the idea of AI enhancing healthcare is nothing new. A Stanford University article published in 1996, talks about how neural networks, like the vast network of neurons in a brain, could predict the likelihood of death from AIDS from a data set of HIV patients much more accurately than other methods used at a time. In a nutshell, AI can be seen as an effective tool to detect and diagnose medical problems, often not visible to human senses, at a much faster rate than any physician – and this is what excites many about its application in healthcare. Go a step further, however, and things start to get a lot more technical.
When looking at neural networks in the context of healthcare, we know that they can be used for diagnosis but what other things can they be used for in the medical field? Well, neural network applications are used in a wide range of things, such as biochemical analysis, when it comes to things like tracking blood glucose, or trying to calculate blood ion levels, or even image analysis for things such as tumor detection or classification of tissues and vessels to determine how much an organ has matured. Additionally, neural networks are used in drug development to treat diseases like cancer and HIV as well as modelling biomolecules. With so many neural networks used in healthcare, which is the most common?
Kohonen networks are a type of neural network that we call self-organizing neural networks. They take data with multiple attributes and then create a two-dimensional visual representation of the data. Kohonen networks can be used to analyze medical data by clustering the data based on different factors such as the patient’s blood type or medical history. For instance, a continent neural network was used to cluster and analyze medical data from patients that did and didn’t have COPD, based on factors such as whether the patient had previous emergency room visits, additional medical problems, and so on. The analysis established a high correlation between being diagnosed with COPD and having respiratory symptoms coupled with other medical problems. The analysis also suggested that patients currently living with respiratory disease or a similar condition should be evaluated much more thoroughly for COPD.
Aside from diagnosis, we can’t talk about healthcare without bringing up the topic of cost. There’s a lot we can say about AI and healthcare costs. But, long story short, things may be looking good with AI and the cost of healthcare. Economic experts claim that AI will help lower the cost of healthcare, as its ability to detect problems earlier than humans, diagnose those problems more efficiently and accurately, and speed up the development of potentially life-saving drugs –ultimately saving us a lot of money.
It seems like AI in the medical field could potentially be very beneficial for us. However, we might not want to get ahead of ourselves just yet, as critics of AI in the medical field do bring up some objections. For starters, critics fear that medical data used to train the AI models and create the algorithms may have some bias in it, which could result in skewed results when the AI model is used for real-world diagnosis. Furthermore, collecting medical data and introducing third parties into the relationship between the physician and the patient, has the potential to destroy the patient’s expectation of confidentiality and responsibility, which is essential in healthcare.
So, ultimately it boils down to two options: providing what may be cost efficient yet improved healthcare, with the risk of sacrificing trust and confidentiality; or we stick with our current health care system but continue to maintain a good relationship between patients and their doctors. As you have seen, neural networks are an irreplaceable component for vital products that combine healthcare and AI together. The biggest challenge will be to find better ways of being able to assess huge amounts of data that are more difficult to interpret and predict. If they’re capable of tweaking this then they’re going to become the change that the healthcare industry needs .